arXiv Machine Learning By Soroor Ghandali

Slow Beats Fast at the Kesten-Stigum Threshold: Minimax, Fisher-Information and Belief-Propagation Characterizations of the Information-Computation Gap in Sparse Stochastic Block Models

Read the original on arXiv Machine Learning →

arXiv:2610. 08872v1 Announce Type: cross Abstract: We study community recovery in the sparse symmetric stochastic block model with $q$ communities, average degree $d$ and signal strength $\lambda$ through statistical decision theory and Fisher information, and obtain three characterizations of the Kesten-Stigum threshold $d\lambda^2=1$ and of the information-computation gap below it.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Statistics ML
1d ago

Community detection with the Bethe-Hessian

arXiv:2411.02835v3 Announce Type: replace-cross Abstract: The Bethe-Hessian matrix, introduced by Saade, Krzakala, and Zdeborov\'a (2014), is a Hermitian matrix designed for applying spectral cluster...

By Ludovic Stephan, Yizhe Zhu
arXiv Statistics ML
4d ago

Bethe Hessian Recovery through the Kesten-Stigum Regime

arXiv:2610. 41802v1 Announce Type: cross Abstract: We show that outlier eigenvectors of the Bethe Hessian achieve weak recovery in the stochastic block model down to the Kesten-Stigum threshold, for every fixed common expected degree $d>1$.

By Dingding Dong, Theo McKenzie